# Lecture 3. Linear Programming. 3B1B Optimization Michaelmas 2015 A. Zisserman. Extreme solutions. Simplex method. Interior point method

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1 Lecture 3 3B1B Optimization Michaelmas 2015 A. Zisserman Linear Programming Extreme solutions Simplex method Interior point method Integer programming and relaxation

2 The Optimization Tree

3 Linear Programming The name is historical, it should really be called Linear Optimization. The problem consists of three parts: A linear function to be maximized maximize f(x) = c 1 x 1 + c 2 x c n x n Problem constraints Non-negative variables subject to a 11 x 1 + a 12 x a 1n x n < b 1 a 21 x 1 + a 22 x a 2n x n < b 2 a m1 x 1 + a m2 x a mn x n < b m e.g. x 1, x 2 > 0

4 Linear Programming The problem is usually expressed in matrix form and then it becomes: Maximize c > x subject to Ax b, x 0 where A is a m n matrix. The objective function, c > x,andconstraints,ax b, are all linear functions of x. (Linear programming problems are convex, so a local optimum is the global optimum.)

5 Example 1 Suppose that a farmer has a piece of farm land, say A square kilometers large, to be planted with either wheat or barley or some combination of the two. The farmer has a limited permissible amount F of fertilizer and P of insecticide which can be used, each of which is required in different amounts per unit area for wheat (F 1, P 1 ) and barley (F 2, P 2 ). Let S 1 be the selling price of wheat, and S 2 the price of barley, and denote the area planted with wheat and barley as x 1 and x 2 respectively. The optimal number of square kilometers to plant with wheat vs. barley can be expressed as a linear programming problem. x 1 x 2 A

6 Example 1 cont. Maximize S 1 x 1 + S 2 x 2 (the revenue this is the objective function ) subject to x 1 +x 2 < A (limit on total area) F 1 x 1 + F 2 x 2 < F (limit on fertilizer) P 1 x 2 + P 2 x 2 < P (limit on insecticide) x 1 >= 0, x 2 > 0 (cannot plant a negative area) which in matrix form becomes maximize subject to

7 Example 2: Max flow Given: a weighted directed graph, source s, destination t Interpret edge weights as capacities Goal: Find maximum flow from s to t Flow does not exceed capacity in any edge Flow at every vertex satisfies equilibrium [ flow in equals flow out ] e.g. oil flowing through pipes, internet routing

8 Example 3 cont. Slide: Robert Sedgewick and Kevin Wayne

9 Example 2 cont.

10 Example 3: shortest path Given: a weighted directed graph, with a single source s Distance from s to v: length of the shortest part from s to v Goal: Find distance (and shortest path) to every vertex e.g. plotting routes on Google maps

11 Application Minimize number of stops (lengths = 1) Minimize amount of time (positive lengths)

12 LP why is it important? We have seen examples of: allocating limited resources network flow shortest path Others include: matching assignment It is a widely applicable problem-solving model because: non-negativity is the usual constraint on any variable that represents an amount of something one is often interested in bounds imposed by limited resources. Dominates world of industry

13 Linear Programming 2D example Cost function: f(x 1,x 2 ) = 0x x 2 Inequality constraints: x 1 + x 2 2 x x x 2 4 max f(x x 1,x 1,x 2 ) 2 x 1,x 2 0

14 Feasible Region Inequality constraints: x x 1 + x 2 =2 x 1 =1.5 x 1 + x 2 2 x x x 2 4 x 1,x x x 2 = x 1

15 LP example max f(x x 1,x 1,x 2 ) 2 Cost function: z z =0.5x 2 f(x 1,x 2 ) = 0x x 2 Inequality constraints: x 1 + x 2 2 x feasible region x 2 x x 2 4 x 1,x 2 0 x 1 extreme point

16 LP example max f(x x 1,x 1,x 2 ) 2 x extreme point contour lines Cost function: 1.6 f(x 1,x 2 ) = 0x x 2 Inequality constraints: x 1 + x 2 2 x x x x 1,x feasible region x 1

17 LP example change of cost function max f(x x 1,x 1,x 2 ) 2 Cost function: f(x 1,x 2 ) = c > x = c 1 x 1 + c 2 x 2 x extreme point contour lines Inequality constraints: x 1 + x 2 2 x x x c x 1,x feasible region x 1

18 Linear Programming optima at vertices The key point is that for any (linear) objective function the optima only occur at the corners (vertices) of the feasible polygonal region (never on the interior region). Similarly, in 3D the optima only occur at the vertices of a polyhedron (and in nd at the vertices of a polytope). However, the optimum is not necessarily unique: it is possible to have a set of optimal solutions covering an edge or face of a polyhedron.

19 Cf Constrained Quadratic Optimization z extreme point 5 x 2 0 x feasible region

20 Linear Programming solution idea So, even though LP is formulated in terms of continuous variables, x, there are only a finite number of discrete possible solutions that need be explored. How to find the maximum?? Try every vertex? But there are too many in large problems. Instead, simply go from one vertex to the next increasing the cost function each time, and in an intelligent manner to avoid having to visit (and test) every vertex. This is the idea of the simplex algorithm

21 Sketch solutions for optimization methods We will look at 2 methods of solution: 1. Simplex method Tableau exploration of vertices based on linear algebra 2. Interior point method Continuous optimization with constraints cast as barriers

22 1. Simplex method Optimum must be at the intersection of constraints Intersections are easy to find, change inequalities to equalities Intersections are called basic solutions 2 x 1 =1.5 some intersections are outside the feasible region (e.g. f) and so need not be considered the others (which are vertices of the feasible region) are called basic feasible solutions a x 1 + x 2 =2 b f x x 2 =4 0.6 c e d

23 Worst complexity There are Cn m m! = (m n)! n! possible solutions, where m is the total number of constraints and n the dimension of the space a x 1 + x 2 =2 x 1 = In this case C 5 2 = 5! (3)! 2! = b f x x 2 =4 However, for large problems the number of solutions can be huge, and it is not realistic to explore them all e c d

24 The Simplex Algorithm Start from a basic feasible solution (i.e. a vertex of feasible region) Consider all the vertices connected to the current one by an edge Choose the vertex which increases the cost function the most (and is still feasible of course) Repeat until no further increases are possible In practice this is very efficient and avoids visiting all vertices

25 Matlab LP Function linprog Linprog() for medium scale problems uses the Simplex algorithm Example Find x that minimizes f(x) = 5x 1 4x 2 6x 3, subject to x 1 x 2 + x x 1 + 2x 2 + 4x x 1 + 2x x 1, 0 x 2, 0 x 3. >> f = [-5; -4; -6]; >> A = [ ]; >> b = [20; 42; 30]; >> lb = zeros(3,1); >> x = linprog(f,a,b,[],[],lb); >> Optimization terminated. >> x x =

26 MOSEK For large scale linear and quadratic optimization problems

27 2. Interior Point Method Solve LP using continuous optimization methods Represent inequalities by barrier functions Follow path through interior of feasible region to vertex c

28 Barrier function method We wish to solve the following problem Minimize subject to f(x) =c > x a i > x b i,i=1...m Problem could be rewritten as Minimize f(x)+ mx i=1 I(a i > x b i ) where I is the indicator function I(u) = 0 for u 0 = for u>0 The difficulty here is that I(u) is not differentiable.

29 Approximation via logarithmic barrier functon Approximate indicator function by logarithmic barrier Minimize f(x) 1 t mx i=1 log( a i > x + b i ) for t>0, (1/t)log( u) is a smooth approximation of I(u) approximation improves as t log( u) t

30 Minimizing function for different Values of t. Function f(x) to be minimized subject to x 1, x 1 Minima converge to the constrained minimum. Function tf(x) log(1 x 2 ) for increasing values of t.

31 Algorithm for Interior Point Method Problem becomes Algorithm Minimize tf(x) mx i=1 log( a i > x + b i ) Solve using Newton s method t μt repeat until convergence As t increases this converges to the solution of the original problem

32 Example Diagram copied from Boyd and Vandenberghe

33 Integer programming There are often situations where the solution is required to be an integer or have boolean values (0 or 1 only). For example: assignment problems scheduling problems matching problems Linear programming can also be used for these cases

34 Example: assignment problem agents tasks Objective: assign n agents to n tasks to minimize total cost Assignment constraints: each agent assigned to one task only each task assigned to one agent only

35 Example: assignment problem agents tasks Objective: assign n agents to n tasks to minimize total cost Assignment constraints: each agent assigned to one task only each task assigned to one agent only

36 cost matrix tasks tasks a g e n t s a g e n t s

37 Example: assignment problem Problem specification x ij is the assignment of agent i to task j (can take values 0 or 1) c ij is the (non-negative) cost of assigning agent i to task j Minimize total cost f(x) = P ij x ij c ij over the n 2 variables x ij tasks j Example solution for x ij each agent i assigned to one task only only one entry in each row each task j assigned to one agent only only one entry in each column a g e n t s i

38 Example: assignment problem Linear Programme formulation min x ij subject to (inequalities) (equalities) f(x) = X ij X j X i x ij c ij tasks j x ij 1, i each agent assigned to at most one task x ij =1, j each task assigned to exactly one agent This is a relaxation of the problem because the variables x ij are not forced to take boolean values. However, it can be shown that the solution x ij only takes the values 0 or 1 a g e n t s i

39 Agents to tasks assignment problem Optimal agents to tasks assignment cost: agents tasks agents tasks Cost of assignment = distance between agent and task e.g. application: tracking, correspondence

40 Example application: tracking pedestrians Multiple Object Tracking using Flow Linear Programming, Berclaz, Fleuret & Fua, 2009

41 What is next? Convexity (when does a function have only a global minimum?) Robust cost functions Stochastic algorithms

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